Abstract
Aspect-level sentiment analysis is a prominent technology in natural language processing (NLP) that analyzes the sentiment polarity of target words in a text. Despite its long history of development, current methods still have some shortcomings. Mainly, they lack the integration of external affective knowledge, which is crucial for allocating attention to aspect-related words in syntactic and semantic information processing. Additionally, the synergy between syntactic and semantic information is often neglected, with most approaches focusing on only one dimension. To address these issues, we propose a knowledge-enhanced dual-channel graph neural network. Our model incorporates external affective knowledge into both the semantic and syntactic channels in different ways, then utilizes a dynamic attention mechanism to fuse information from these channels. We conducted experiments on Semeval2014, 2015, and 2016 datasets, and the results showed significant improvements compared to existing methods. Our approach bridges the gaps in current techniques and enhances performance in aspect-level sentiment analysis.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Liang, B., Su, H., Gui, L., Cambria, E., Xu, R.: Aspect-based sentiment analysis via affective knowledge enhanced graph convolutional networks. Knowl.-Based Syst. 235, 107643 (2022). https://doi.org/10.1016/j.knosys.2021.107643
Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP) , pp. 4567–4577. Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1464
Li, R., Chen, H., Feng, F., Ma, Z., Wang, X., Hovy, E.: Dual graph convolutional networks for aspect-based sentiment analysis, p. 11 (2011)
Xie, Y., Pu, P.: How commonsense knowledge helps with natural language tasks: a survey of recent resources and methodologies. arXiv, 10 August 2021. https://doi.org/10.48550/arXiv.2108.04674
Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: aspect based sentiment analysis (2016)
Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: SemEval-2015 task 12: aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), June 2015, pp. 486–495. Association for Computational Linguistics, Denver, Colorado (2015). https://doi.org/10.18653/v1/S15-2082
Pontiki, M., et al.: SemEval-2016 task 5: aspect based sentiment analysis, p. 12 (2016)
Brun, C., Popa, D.N., Roux, C.: XRCE: hybrid classification for aspect-based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 838–842. Association for Computational Linguistics Dublin, Ireland (2014). https://doi.org/10.3115/v1/S14-2149
Vo, D.-T., Zhang, Y.: Target-dependent Twitter sentiment classification with rich automatic features, p. 7 (2015)
Ding, X., Liu, B., Yu, P.S.: A holistic lexicon-based approach to opinion mining. In: Proceedings of the International Conference on Web Search and Web Data Mining - WSDM ‘08, p. 231, Palo Alto, California, USA. ACM Press (2008). https://doi.org/10.1145/1341531.1341561
Liu, B., Blasch, E., Chen, Y., Shen, D., Chen, G.: Scalable sentiment classification for Big Data analysis using Naive Bayes Classifier. In: 2013 IEEE International Conference on Big Data, Silicon Valley, CA, USA. IEEE, October 2013, pp. 99–104 (2013). https://doi.org/10.1109/BigData.2013.6691740
Pang, B., Lee, L., Vaithyanathan, S.: Thumbs up?: Sentiment classification using machine learning techniques. In: Proceedings of the ACL-02 Conference on Empirical Methods in Natural Language Processing - EMNLP ’02, pp. 79–86. Association for Computational Linguistics (2002). https://doi.org/10.3115/1118693.1118704
Mikolov, T., Chen, K., Corrado, G., Dean, J.: Efficient estimation of word representations in vector space. arXiv, 06 September 2013. http://arxiv.org/abs/1301.3781. Accessed 08 December 2022
Pennington, J., Socher, R., Manning, C.: Glove: global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543. Association for Computational Linguistics, Doha, Qatar (2014). https://doi.org/10.3115/v1/D14-1162
Tang, D., Qin, B., Feng, X., Liu, T.: Effective LSTMs for target-dependent sentiment classification, p. 10 (2015)
Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. In: Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing, pp. 214–224. Association for Computational Linguistics, Austin, Texas (2016). https://doi.org/10.18653/v1/D16-1021
Ma, D., Li, S., Zhang, X., Wang, H.: Interactive attention networks for aspect-level sentiment classification. arXiv, 04 September 2017. http://arxiv.org/abs/1709.00893. Accessed 14 Dec 2022
Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. arXiv, 24 May 2019. http://arxiv.org/abs/1810.04805. Accessed 14 Dec 2022
Song, Y., Wang, J., Jiang, T., Liu, Z., Rao, Y.: Attentional encoder network for targeted sentiment classification, pp. 93–103 (2019). https://doi.org/10.1007/978-3-030-30490-4_9
Sun, C., Huang, L., Qiu, X.: Utilizing BERT for aspect-based sentiment analysis via constructing auxiliary sentence (2019)
Li, X., Bing, L., Zhang, W., Lam, W.: Exploiting BERT for end-to-end aspect-based sentiment analysis. In: Proceedings of the 5th Workshop on Noisy User-Generated Text (W-NUT 2019), pp. 34–41. Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-5505
Sun, K., Zhang, R., Mensah, S., Mao, Y., Liu, X.: Aspect-level sentiment analysis via convolution over dependency tree. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 5678–5687. Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1569
Liang, B., Yin, R., Gui, L., Du, J., Xu, R.: Jointly learning aspect-focused and inter-aspect relations with graph convolutional networks for aspect sentiment analysis. In: Proceedings of the 28th International Conference on Computational Linguistics, pp. 150–161. International Committee on Computational Linguistics, Barcelona, Spain (Online) (2020). https://doi.org/10.18653/v1/2020.coling-main.13
Wang, K., Shen, W., Yang, Y., Quan, X., Wang, R.: Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3229–3238. Association for Computational Linguistics (2020). https://doi.org/10.18653/v1/2020.acl-main.295
Cambria, E., Poria, S., Hazarika, D., Kwok, K.: SenticNet 5: discovering conceptual primitives for sentiment analysis by means of context embeddings. In: AAAI, vol. 32, no. 1, April 2018. https://doi.org/10.1609/aaai.v32i1.11559
Yang, P., Li, L., Luo, F., Liu, T., Sun, X.: Enhancing topic-to-essay generation with external commonsense knowledge. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 2002–2012. Association for Computational Linguistics, Florence, Italy (2019). doi: https://doi.org/10.18653/v1/P19-1193
Li, Y., Pan, Q., Yang, T., Wang, S., Tang, J., Cambria, E.: Learning word representations for sentiment analysis. Cogn. Comput. 9(6), 843–851 (2017). https://doi.org/10.1007/s12559-017-9492-2
Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: AAAI, vol. 32, no. 1, April 2018. https://doi.org/10.1609/aaai.v32i1.12048
Li, Y., Sun, X., Wang, M.: Embedding extra knowledge and a dependency tree based on a graph attention network for aspect-based sentiment analysis. In: 2021 International Joint Conference on Neural Networks (IJCNN), pp. 1–8. IEEE, Shenzhen, China July 2021. https://doi.org/10.1109/IJCNN52387.2021.9533695
Cambria, E., Fu, J., Bisio, F., Poria, S.: AffectiveSpace 2: enabling affective intuition for concept-level sentiment analysis. In: AAAI, vol. 29, no. 1, February 2015. https://doi.org/10.1609/aaai.v29i1.9230
Goldberg, Y.: Assessing BERT’s syntactic abilities. arXiv, 16 January 2019. http://arxiv.org/abs/1901.05287. Accessed 29 June 2023
Htut, P.M., Phang, J., Bordia, S., Bowman, S.R.: Do attention heads in BERT track syntactic dependencies? arXiv, 27 November 2019. http://arxiv.org/abs/1911.12246. Accessed 29 June 2023
Cambria, E., Hussain, A.: Sentic Computing. SpringerBriefs in Cognitive Computation, vol. 2. Springer, Dordrecht (2012). https://doi.org/10.1007/978-94-007-5070-8
Wang, Y., Huang, M., Zhu, X., Zhao, L.: Attention-based LSTM for aspect-level sentiment classification. In: Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing, pp. 606–615. Association for Computational Linguistics, Austin, Texas (2016). https://doi.org/10.18653/v1/D16-1058
Jawahar, G., Sagot, B., Seddah, D.: What Does BERT Learn about the Structure of Language? In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, Florence, Italy, pp. 3651–3657 (2019). https://doi.org/10.18653/v1/P19-1356
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2024 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Jin, H., Zhang, Q., Liang, X., Zhou, Y., Li, W. (2024). Dual Channel Graph Neural Network Enhanced by External Affective Knowledge for Aspect Level Sentiment Analysis. In: Luo, B., Cheng, L., Wu, ZG., Li, H., Li, C. (eds) Neural Information Processing. ICONIP 2023. Lecture Notes in Computer Science, vol 14448. Springer, Singapore. https://doi.org/10.1007/978-981-99-8082-6_20
Download citation
DOI: https://doi.org/10.1007/978-981-99-8082-6_20
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-99-8081-9
Online ISBN: 978-981-99-8082-6
eBook Packages: Computer ScienceComputer Science (R0)